Characterization of Sulfur Compounds in Oilsands Bitumen by Methylation Followed by Positive-Ion Electrospray Ionization and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
Sulfur compounds in Canadian oilsands bitumen were reacted with methyl iodide in the presence of silver tetrafluoroborate and converted to methylsulfonium salts. The methylsulfonium salts were characterized by positive-ion electrospray ionization (ESI) and Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS). Heteroatoms were characterized by their class (number of nitrogen, oxygen, and sulfur heteroatoms), type [rings plus double bonds (DBE)], and carbon number distribution. The S 1, S 2, S 3, O 1 S 1, O 1 S 2, O 2 S 1, and N 1 S 1 sulfur-containing class species were identified in bitumen-derived methylsulfonium salts. The molecular weights of the sulfur compounds were varied from 200 to 700 Da. The S x class species were the predominant heteroatom compounds. The S 1, S 2, and S 3 class species comprised 74%, 11%, and 1%, respectively, of the total identified species. As the sulfur atom number increased, the DBE of the abundant S x class species shifted to a higher value. Sulfur species identified from the spectrum had a DBE value of less than 20. The potential molecular structures of heteroatom classes were inferred from the DBE distribution and carbon number data. Methylation followed by ESI MS is an effective technique for sulfur speciation of bitumen.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".